Overview

Dataset statistics

Number of variables37
Number of observations1053
Missing cells26
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory508.2 KiB
Average record size in memory494.2 B

Variable types

Categorical8
Numeric27
DateTime1
Boolean1

Dataset

DescriptionQuality-verified clinical data for JHB_Ezin_002
CreatorHEAT Research Programme
AuthorRP2 Clinical Data Team
URLhttps://github.com/Logic06183/RP2_dataoverview

Variable descriptions

study_sourceStudy identifier
Age (at enrolment)Patient age at study enrollment
SexBiological sex
RaceRacial/ethnic group
enrollment_dateDate of study enrollment
visit_dateDate of clinic visit
primary_datePrimary reference date
study_armStudy treatment arm
study_visitStudy visit number
Antiretroviral Therapy StatusCurrent ART status
BMI (kg/m²)Body Mass Index
weight_kgBody weight in kilograms
height_mHeight in meters
Waist circumference (cm)Waist circumference in centimeters
hip_circumference_cmHip circumference in centimeters
waist_hip_ratioWaist-to-hip ratio
systolic_bp_mmHgSystolic blood pressure
diastolic_bp_mmHgDiastolic blood pressure
heart_rate_bpmHeart rate in beats per minute
Respiratory rate (breaths/min)Respiratory rate
Oxygen saturation (%)Oxygen saturation
body_temperature_celsiusBody temperature in Celsius
CD4 cell count (cells/µL)CD4+ T lymphocyte count
HIV viral load (copies/mL)HIV RNA copies per mL
cd4_percentCD4+ percentage
cd8_count_cells_uLCD8+ T lymphocyte count
cd4_cd8_ratioCD4/CD8 ratio
Hematocrit (%)Hematocrit
hemoglobin_g_dLHemoglobin concentration
White blood cell count (×10³/µL)Total WBC count
Red blood cell count (×10⁶/µL)Total RBC count
Platelet count (×10³/µL)Platelet count
MCV (MEAN CELL VOLUME)Mean corpuscular volume
mch_pgMean corpuscular hemoglobin
mchc_g_dLMean corpuscular hemoglobin concentration
RDWRed cell distribution width
Lymphocyte count (×10⁹/L)Lymphocyte absolute count
Neutrophil count (×10⁹/L)Neutrophil absolute count
Monocyte count (×10⁹/L)Monocyte absolute count
Eosinophil count (×10⁹/L)Eosinophil absolute count
Basophil count (×10⁹/L)Basophil absolute count
lymphocyte_percentLymphocyte percentage
neutrophil_percentNeutrophil percentage
monocyte_percentMonocyte percentage
eosinophil_percentEosinophil percentage
basophil_percentBasophil percentage
ALT (U/L)Alanine aminotransferase
AST (U/L)Aspartate aminotransferase
Alkaline phosphatase (U/L)Alkaline phosphatase
Total bilirubin (mg/dL)Total bilirubin
direct_bilirubin_mg_dLDirect bilirubin
indirect_bilirubin_mg_dLIndirect bilirubin
Albumin (g/dL)Serum albumin
Total protein (g/dL)Total serum protein
ggt_u_LGamma-glutamyl transferase
creatinine_umol_LSerum creatinine (µmol/L)
creatinine_mg_dLSerum creatinine (mg/dL)
creatinine clearanceEstimated creatinine clearance
bun_mg_dLBlood urea nitrogen
urea_mmol_LSerum urea
egfr_ml_minEstimated glomerular filtration rate
Sodium (mEq/L)Serum sodium
Potassium (mEq/L)Serum potassium
chloride_mEq_LSerum chloride
bicarbonate_mEq_LSerum bicarbonate
calcium_mg_dLSerum calcium
magnesium_mg_dLSerum magnesium
phosphate_mg_dLSerum phosphate
total_cholesterol_mg_dLTotal cholesterol
hdl_cholesterol_mg_dLHDL cholesterol
ldl_cholesterol_mg_dLLDL cholesterol
Triglycerides (mg/dL)Triglycerides
vldl_cholesterol_mg_dLVLDL cholesterol
cholesterol_hdl_ratioTotal cholesterol/HDL ratio
fasting_glucose_mmol_LFasting blood glucose (mmol/L)
glucose_mg_dLBlood glucose (mg/dL)
hba1c_percentGlycated hemoglobin
insulin_uIU_mLSerum insulin
lactate_mmol_LBlood lactate
crp_mg_LC-reactive protein
esr_mm_hrErythrocyte sedimentation rate
pt_secondsProthrombin time
inrInternational normalized ratio
aptt_secondsActivated partial thromboplastin time
uric_acid_mg_dLSerum uric acid
ldh_u_LLactate dehydrogenase
ck_u_LCreatine kinase
amylase_u_LSerum amylase
lipase_u_LSerum lipase
climate_daily_mean_tempDaily mean temperature
climate_daily_max_tempDaily maximum temperature
climate_daily_min_tempDaily minimum temperature
climate_temp_anomalyTemperature anomaly from baseline
climate_heat_day_p90Heat day indicator (>90th percentile)
climate_heat_day_p95Heat day indicator (>95th percentile)
climate_heat_stress_indexHeat stress index
climate_humidityRelative humidity
climate_precipitationPrecipitation
climate_seasonSeason
cd4_correction_appliedQuality flag: CD4 corrections applied
final_comprehensive_fix_appliedQuality flag: Comprehensive corrections applied
waist_circ_unit_correction_appliedQuality flag: Waist circumference unit corrected
sa_biomarker_standardsSouth African biomarker reference standards applied

Alerts

study_source has constant value "JHB_Ezin_002"Constant
cd4_correction_applied has constant value "0.0"Constant
final_comprehensive_fix_applied has constant value "1.0"Constant
waist_circ_unit_correction_applied has constant value "False"Constant
sa_biomarker_standards has constant value "1.0"Constant
climate_heat_day_p90 has constant value "0.0"Constant
climate_heat_day_p95 has constant value "0.0"Constant
BMI (kg/m²) is highly overall correlated with weight_kgHigh correlation
CD4 cell count (cells/µL) is highly overall correlated with HIV viral load (copies/mL)High correlation
HIV viral load (copies/mL) is highly overall correlated with CD4 cell count (cells/µL)High correlation
Hematocrit (%) is highly overall correlated with Red blood cell count (×10⁶/µL) and 2 other fieldsHigh correlation
Lymphocyte count (×10⁹/L) is highly overall correlated with White blood cell count (×10³/µL)High correlation
Monocyte count (×10⁹/L) is highly overall correlated with White blood cell count (×10³/µL)High correlation
Neutrophil count (×10⁹/L) is highly overall correlated with White blood cell count (×10³/µL)High correlation
Red blood cell count (×10⁶/µL) is highly overall correlated with Hematocrit (%) and 1 other fieldsHigh correlation
Sex is highly overall correlated with Hematocrit (%) and 2 other fieldsHigh correlation
White blood cell count (×10³/µL) is highly overall correlated with Lymphocyte count (×10⁹/L) and 2 other fieldsHigh correlation
climate_daily_max_temp is highly overall correlated with climate_daily_mean_temp and 3 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with climate_daily_max_temp and 3 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with climate_daily_max_temp and 3 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with climate_daily_max_temp and 2 other fieldsHigh correlation
climate_season is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with climate_daily_min_temp and 1 other fieldsHigh correlation
diastolic_bp_mmHg is highly overall correlated with systolic_bp_mmHgHigh correlation
height_m is highly overall correlated with SexHigh correlation
hemoglobin_g_dL is highly overall correlated with Hematocrit (%) and 2 other fieldsHigh correlation
mch_pg is highly overall correlated with mchc_g_dLHigh correlation
mchc_g_dL is highly overall correlated with mch_pgHigh correlation
systolic_bp_mmHg is highly overall correlated with diastolic_bp_mmHgHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²)High correlation
Eosinophil count (×10⁹/L) has 47 (4.5%) zerosZeros
Basophil count (×10⁹/L) has 122 (11.6%) zerosZeros

Reproduction

Analysis started2025-11-25 05:10:44.798757
Analysis finished2025-11-25 05:11:10.847459
Duration26.05 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Study identifier

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
JHB_Ezin_002
1053 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12636
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_Ezin_002
2nd rowJHB_Ezin_002
3rd rowJHB_Ezin_002
4th rowJHB_Ezin_002
5th rowJHB_Ezin_002

Common Values

ValueCountFrequency (%)
JHB_Ezin_0021053
100.0%

Length

2025-11-25T07:11:10.870037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:10.900014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_ezin_0021053
100.0%

Most occurring characters

ValueCountFrequency (%)
_2106
16.7%
02106
16.7%
J1053
8.3%
H1053
8.3%
B1053
8.3%
E1053
8.3%
z1053
8.3%
i1053
8.3%
n1053
8.3%
21053
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4212
33.3%
Decimal Number3159
25.0%
Lowercase Letter3159
25.0%
Connector Punctuation2106
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J1053
25.0%
H1053
25.0%
B1053
25.0%
E1053
25.0%
Lowercase Letter
ValueCountFrequency (%)
z1053
33.3%
i1053
33.3%
n1053
33.3%
Decimal Number
ValueCountFrequency (%)
02106
66.7%
21053
33.3%
Connector Punctuation
ValueCountFrequency (%)
_2106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7371
58.3%
Common5265
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
J1053
14.3%
H1053
14.3%
B1053
14.3%
E1053
14.3%
z1053
14.3%
i1053
14.3%
n1053
14.3%
Common
ValueCountFrequency (%)
_2106
40.0%
02106
40.0%
21053
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_2106
16.7%
02106
16.7%
J1053
8.3%
H1053
8.3%
B1053
8.3%
E1053
8.3%
z1053
8.3%
i1053
8.3%
n1053
8.3%
21053
8.3%

Age (at enrolment)
Real number (ℝ)

Patient age at study enrollment

Distinct45
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.460589
Minimum13
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:10.934073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile21
Q127
median32
Q337
95-th percentile46
Maximum62
Range49
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7363651
Coefficient of variation (CV)0.23833101
Kurtosis0.1552562
Mean32.460589
Median Absolute Deviation (MAD)5
Skewness0.52185785
Sum34181
Variance59.851345
MonotonicityNot monotonic
2025-11-25T07:11:10.976645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
3166
 
6.3%
3356
 
5.3%
2955
 
5.2%
3053
 
5.0%
3250
 
4.7%
2649
 
4.7%
3548
 
4.6%
3448
 
4.6%
2447
 
4.5%
2847
 
4.5%
Other values (35)534
50.7%
ValueCountFrequency (%)
131
 
0.1%
141
 
0.1%
152
 
0.2%
171
 
0.1%
189
 
0.9%
198
 
0.8%
2017
1.6%
2119
1.8%
2226
2.5%
2331
2.9%
ValueCountFrequency (%)
621
 
0.1%
591
 
0.1%
572
 
0.2%
552
 
0.2%
544
0.4%
535
0.5%
523
0.3%
517
0.7%
504
0.4%
492
 
0.2%

Sex
Categorical

High correlation 

Biological sex

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
Female
627 
Male
426 

Length

Max length6
Median length6
Mean length5.1908832
Min length4

Characters and Unicode

Total characters5466
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female627
59.5%
Male426
40.5%

Length

2025-11-25T07:11:11.103842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:11.139378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female627
59.5%
male426
40.5%

Most occurring characters

ValueCountFrequency (%)
e1680
30.7%
a1053
19.3%
l1053
19.3%
F627
 
11.5%
m627
 
11.5%
M426
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4413
80.7%
Uppercase Letter1053
 
19.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1680
38.1%
a1053
23.9%
l1053
23.9%
m627
 
14.2%
Uppercase Letter
ValueCountFrequency (%)
F627
59.5%
M426
40.5%

Most occurring scripts

ValueCountFrequency (%)
Latin5466
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1680
30.7%
a1053
19.3%
l1053
19.3%
F627
 
11.5%
m627
 
11.5%
M426
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1680
30.7%
a1053
19.3%
l1053
19.3%
F627
 
11.5%
m627
 
11.5%
M426
 
7.8%

primary_date
Date

Primary reference date

Distinct268
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
Minimum2017-01-17 00:00:00
Maximum2018-05-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:11:11.176963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:11.222705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BMI (kg/m²)
Real number (ℝ)

High correlation 

Body Mass Index

Distinct1016
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.11742
Minimum15.269471
Maximum49.672814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:11.266026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.269471
5-th percentile17.880777
Q120.15625
median23.084852
Q326.794938
95-th percentile33.960395
Maximum49.672814
Range34.403343
Interquartile range (IQR)6.638688

Descriptive statistics

Standard deviation5.3163571
Coefficient of variation (CV)0.2204364
Kurtosis2.3289041
Mean24.11742
Median Absolute Deviation (MAD)3.2616416
Skewness1.2722863
Sum25395.643
Variance28.263653
MonotonicityNot monotonic
2025-11-25T07:11:11.313861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.805922492
 
0.2%
21.287770042
 
0.2%
22.817460322
 
0.2%
20.661157022
 
0.2%
19.887258852
 
0.2%
20.861119662
 
0.2%
19.395918372
 
0.2%
24.241544492
 
0.2%
21.241004922
 
0.2%
24.394463672
 
0.2%
Other values (1006)1033
98.1%
ValueCountFrequency (%)
15.269471081
0.1%
15.338972351
0.1%
15.377500291
0.1%
15.445162251
0.1%
15.480864371
0.1%
15.495867771
0.1%
15.50173011
0.1%
15.515143321
0.1%
15.561339971
0.1%
15.637645971
0.1%
ValueCountFrequency (%)
49.67281381
0.1%
47.498964961
0.1%
47.199265381
0.1%
47.075962541
0.1%
45.800944981
0.1%
45.28925621
0.1%
44.90195341
0.1%
44.708956311
0.1%
43.490608141
0.1%
40.731875531
0.1%

weight_kg
Real number (ℝ)

High correlation 

Body weight in kilograms

Distinct449
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.773694
Minimum41.3
Maximum133.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:11.362192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41.3
5-th percentile49.4
Q158.7
median66.4
Q377
95-th percentile96.42
Maximum133.6
Range92.3
Interquartile range (IQR)18.3

Descriptive statistics

Standard deviation14.262708
Coefficient of variation (CV)0.20738609
Kurtosis1.5419145
Mean68.773694
Median Absolute Deviation (MAD)8.8
Skewness1.0350275
Sum72418.7
Variance203.42483
MonotonicityNot monotonic
2025-11-25T07:11:11.408783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.410
 
0.9%
59.410
 
0.9%
71.18
 
0.8%
68.18
 
0.8%
66.87
 
0.7%
66.17
 
0.7%
64.47
 
0.7%
57.46
 
0.6%
61.56
 
0.6%
48.56
 
0.6%
Other values (439)978
92.9%
ValueCountFrequency (%)
41.31
0.1%
42.81
0.1%
431
0.1%
43.21
0.1%
43.31
0.1%
43.81
0.1%
441
0.1%
44.41
0.1%
44.81
0.1%
45.41
0.1%
ValueCountFrequency (%)
133.61
0.1%
128.51
0.1%
126.21
0.1%
123.31
0.1%
123.21
0.1%
1211
0.1%
120.21
0.1%
119.31
0.1%
117.41
0.1%
117.31
0.1%

height_m
Real number (ℝ)

High correlation 

Height in meters

Distinct57
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6932479
Minimum1.32
Maximum2.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:11.453877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.32
5-th percentile1.54
Q11.62
median1.69
Q31.77
95-th percentile1.86
Maximum2.01
Range0.69
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.1001059
Coefficient of variation (CV)0.059120639
Kurtosis-0.019764094
Mean1.6932479
Median Absolute Deviation (MAD)0.07
Skewness0.14414148
Sum1782.99
Variance0.01002119
MonotonicityNot monotonic
2025-11-25T07:11:11.502977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6448
 
4.6%
1.7746
 
4.4%
1.6742
 
4.0%
1.6542
 
4.0%
1.741
 
3.9%
1.6641
 
3.9%
1.7139
 
3.7%
1.6239
 
3.7%
1.6338
 
3.6%
1.6936
 
3.4%
Other values (47)641
60.9%
ValueCountFrequency (%)
1.321
 
0.1%
1.351
 
0.1%
1.361
 
0.1%
1.442
 
0.2%
1.452
 
0.2%
1.461
 
0.1%
1.482
 
0.2%
1.56
0.6%
1.516
0.6%
1.529
0.9%
ValueCountFrequency (%)
2.012
 
0.2%
1.991
 
0.1%
1.971
 
0.1%
1.962
 
0.2%
1.951
 
0.1%
1.941
 
0.1%
1.935
0.5%
1.923
0.3%
1.913
0.3%
1.94
0.4%

systolic_bp_mmHg
Real number (ℝ)

High correlation 

Systolic blood pressure

Distinct89
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.9867
Minimum87
Maximum204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:11.550952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum87
5-th percentile100
Q1112
median122
Q3132
95-th percentile148
Maximum204
Range117
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.366555
Coefficient of variation (CV)0.12494484
Kurtosis2.2558664
Mean122.9867
Median Absolute Deviation (MAD)10
Skewness0.8395301
Sum129505
Variance236.131
MonotonicityNot monotonic
2025-11-25T07:11:11.596654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13036
 
3.4%
11933
 
3.1%
12132
 
3.0%
12431
 
2.9%
11631
 
2.9%
11131
 
2.9%
13630
 
2.8%
11030
 
2.8%
13230
 
2.8%
13728
 
2.7%
Other values (79)741
70.4%
ValueCountFrequency (%)
871
 
0.1%
891
 
0.1%
901
 
0.1%
913
 
0.3%
922
 
0.2%
931
 
0.1%
944
0.4%
955
0.5%
969
0.9%
975
0.5%
ValueCountFrequency (%)
2041
0.1%
1972
0.2%
1901
0.1%
1851
0.1%
1761
0.1%
1741
0.1%
1731
0.1%
1722
0.2%
1702
0.2%
1692
0.2%

diastolic_bp_mmHg
Real number (ℝ)

High correlation 

Diastolic blood pressure

Distinct74
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.189934
Minimum39
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:11.643056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile61
Q171
median79
Q385
95-th percentile100
Maximum132
Range93
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.915419
Coefficient of variation (CV)0.15046633
Kurtosis1.5170025
Mean79.189934
Median Absolute Deviation (MAD)7
Skewness0.64779312
Sum83387
Variance141.9772
MonotonicityNot monotonic
2025-11-25T07:11:11.689419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8252
 
4.9%
7951
 
4.8%
8544
 
4.2%
8344
 
4.2%
8043
 
4.1%
8842
 
4.0%
7238
 
3.6%
7735
 
3.3%
8435
 
3.3%
7335
 
3.3%
Other values (64)634
60.2%
ValueCountFrequency (%)
391
 
0.1%
502
 
0.2%
512
 
0.2%
523
0.3%
544
0.4%
554
0.4%
561
 
0.1%
573
0.3%
585
0.5%
594
0.4%
ValueCountFrequency (%)
1321
 
0.1%
1272
0.2%
1231
 
0.1%
1211
 
0.1%
1201
 
0.1%
1181
 
0.1%
1173
0.3%
1161
 
0.1%
1151
 
0.1%
1142
0.2%

heart_rate_bpm
Real number (ℝ)

Heart rate in beats per minute

Distinct71
Distinct (%)6.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean78.706274
Minimum50
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:11.732978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile58
Q169
median78
Q388
95-th percentile102
Maximum135
Range85
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.512169
Coefficient of variation (CV)0.17167843
Kurtosis0.11364238
Mean78.706274
Median Absolute Deviation (MAD)10
Skewness0.36895181
Sum82799
Variance182.57872
MonotonicityNot monotonic
2025-11-25T07:11:11.776453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7439
 
3.7%
8135
 
3.3%
7834
 
3.2%
8232
 
3.0%
8832
 
3.0%
7930
 
2.8%
7630
 
2.8%
6930
 
2.8%
8929
 
2.8%
8029
 
2.8%
Other values (61)732
69.5%
ValueCountFrequency (%)
503
 
0.3%
512
 
0.2%
526
0.6%
536
0.6%
5410
0.9%
556
0.6%
5610
0.9%
576
0.6%
5813
1.2%
5914
1.3%
ValueCountFrequency (%)
1351
0.1%
1301
0.1%
1241
0.1%
1221
0.1%
1191
0.1%
1171
0.1%
1152
0.2%
1141
0.1%
1121
0.1%
1112
0.2%

body_temperature_celsius
Real number (ℝ)

Body temperature in Celsius

Distinct27
Distinct (%)2.6%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean36.519962
Minimum36
Maximum39.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:11.816519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile36
Q136.15
median36.4
Q336.8
95-th percentile37.3
Maximum39.9
Range3.9
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.45661989
Coefficient of variation (CV)0.012503296
Kurtosis6.5203939
Mean36.519962
Median Absolute Deviation (MAD)0.3
Skewness1.6311046
Sum38236.4
Variance0.20850172
MonotonicityNot monotonic
2025-11-25T07:11:11.860123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
36150
14.2%
36.1112
10.6%
36.2100
9.5%
36.798
9.3%
36.487
8.3%
36.379
7.5%
36.573
6.9%
36.867
6.4%
36.664
6.1%
3760
 
5.7%
Other values (17)157
14.9%
ValueCountFrequency (%)
36150
14.2%
36.1112
10.6%
36.2100
9.5%
36.379
7.5%
36.487
8.3%
36.573
6.9%
36.664
6.1%
36.798
9.3%
36.867
6.4%
36.947
 
4.5%
ValueCountFrequency (%)
39.91
 
0.1%
39.71
 
0.1%
39.11
 
0.1%
38.91
 
0.1%
38.51
 
0.1%
38.42
 
0.2%
38.11
 
0.1%
37.91
 
0.1%
37.81
 
0.1%
37.77
0.7%

CD4 cell count (cells/µL)
Real number (ℝ)

High correlation 

CD4+ T lymphocyte count

Distinct903
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.014084
Minimum0.26
Maximum54.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:11.912678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile3.792
Q111.28
median17.08
Q323.62
95-th percentile34.336
Maximum54.13
Range53.87
Interquartile range (IQR)12.34

Descriptive statistics

Standard deviation9.1601515
Coefficient of variation (CV)0.50849944
Kurtosis-0.0048244797
Mean18.014084
Median Absolute Deviation (MAD)6.21
Skewness0.44891061
Sum18968.83
Variance83.908375
MonotonicityNot monotonic
2025-11-25T07:11:11.965350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.544
 
0.4%
22.684
 
0.4%
17.673
 
0.3%
16.53
 
0.3%
14.993
 
0.3%
5.583
 
0.3%
12.843
 
0.3%
16.933
 
0.3%
10.493
 
0.3%
17.743
 
0.3%
Other values (893)1021
97.0%
ValueCountFrequency (%)
0.261
0.1%
0.341
0.1%
0.451
0.1%
0.531
0.1%
0.631
0.1%
0.651
0.1%
0.71
0.1%
0.771
0.1%
0.831
0.1%
0.911
0.1%
ValueCountFrequency (%)
54.131
0.1%
50.631
0.1%
481
0.1%
44.711
0.1%
44.651
0.1%
44.611
0.1%
43.651
0.1%
43.071
0.1%
42.381
0.1%
41.451
0.1%

HIV viral load (copies/mL)
Real number (ℝ)

High correlation 

HIV RNA copies per mL

Distinct977
Distinct (%)93.0%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean88703.72
Minimum0
Maximum4117370
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.014210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile971.5
Q15839.5
median24796
Q379879
95-th percentile361649.5
Maximum4117370
Range4117370
Interquartile range (IQR)74039.5

Descriptive statistics

Standard deviation231648.72
Coefficient of variation (CV)2.6114882
Kurtosis117.98199
Mean88703.72
Median Absolute Deviation (MAD)21970
Skewness8.9610023
Sum93227610
Variance5.3661128 × 1010
MonotonicityNot monotonic
2025-11-25T07:11:12.060830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
839803
 
0.3%
39553
 
0.3%
120493
 
0.3%
6412
 
0.2%
8892
 
0.2%
3763632
 
0.2%
375252
 
0.2%
36922
 
0.2%
1071472
 
0.2%
7652
 
0.2%
Other values (967)1028
97.6%
ValueCountFrequency (%)
01
0.1%
5011
0.1%
5051
0.1%
5121
0.1%
5271
0.1%
5281
0.1%
5351
0.1%
5411
0.1%
5531
0.1%
5561
0.1%
ValueCountFrequency (%)
41173701
0.1%
27572981
0.1%
23246901
0.1%
17604591
0.1%
15519881
0.1%
13932131
0.1%
12039331
0.1%
11350111
0.1%
11103452
0.2%
10005911
0.1%

Hematocrit (%)
Real number (ℝ)

High correlation 

Hematocrit

Distinct39
Distinct (%)3.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean40.671958
Minimum21
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.103520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q137
median41
Q344
95-th percentile49
Maximum54
Range33
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.326574
Coefficient of variation (CV)0.13096429
Kurtosis0.29229504
Mean40.671958
Median Absolute Deviation (MAD)3
Skewness-0.47071382
Sum42786.9
Variance28.372391
MonotonicityNot monotonic
2025-11-25T07:11:12.147245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4289
 
8.5%
4188
 
8.4%
4381
 
7.7%
4080
 
7.6%
4571
 
6.7%
3967
 
6.4%
3765
 
6.2%
4464
 
6.1%
3856
 
5.3%
3650
 
4.7%
Other values (29)341
32.4%
ValueCountFrequency (%)
211
 
0.1%
232
 
0.2%
254
 
0.4%
266
 
0.6%
273
 
0.3%
287
0.7%
2914
1.3%
3013
1.2%
3117
1.6%
3214
1.3%
ValueCountFrequency (%)
544
 
0.4%
531
 
0.1%
521
 
0.1%
516
 
0.6%
5027
2.6%
4920
1.9%
4837
3.5%
47.81
 
0.1%
4742
4.0%
4642
4.0%

hemoglobin_g_dL
Real number (ℝ)

High correlation 

Hemoglobin concentration

Distinct103
Distinct (%)9.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.298669
Minimum6.1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.192809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile9.9
Q112.3
median13.4
Q314.6
95-th percentile16.1
Maximum18
Range11.9
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.8591093
Coefficient of variation (CV)0.13979664
Kurtosis0.50078028
Mean13.298669
Median Absolute Deviation (MAD)1.2
Skewness-0.54074656
Sum13990.2
Variance3.4562875
MonotonicityNot monotonic
2025-11-25T07:11:12.242909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.432
 
3.0%
12.530
 
2.8%
13.928
 
2.7%
13.827
 
2.6%
13.325
 
2.4%
13.524
 
2.3%
13.224
 
2.3%
13.624
 
2.3%
14.424
 
2.3%
14.723
 
2.2%
Other values (93)791
75.1%
ValueCountFrequency (%)
6.11
 
0.1%
6.81
 
0.1%
7.21
 
0.1%
7.31
 
0.1%
7.41
 
0.1%
7.61
 
0.1%
7.73
0.3%
7.91
 
0.1%
83
0.3%
8.21
 
0.1%
ValueCountFrequency (%)
181
0.1%
17.81
0.1%
17.61
0.1%
17.51
0.1%
17.41
0.1%
17.32
0.2%
17.21
0.1%
17.11
0.1%
172
0.2%
16.92
0.2%

White blood cell count (×10³/µL)
Real number (ℝ)

High correlation 

Total WBC count

Distinct496
Distinct (%)47.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.261635
Minimum1.52
Maximum24.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.289547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.52
5-th percentile2.9455
Q14.0475
median4.95
Q36.1
95-th percentile8.6335
Maximum24.68
Range23.16
Interquartile range (IQR)2.0525

Descriptive statistics

Standard deviation1.9325762
Coefficient of variation (CV)0.36729576
Kurtosis13.505495
Mean5.261635
Median Absolute Deviation (MAD)1.03
Skewness2.3037535
Sum5535.24
Variance3.7348508
MonotonicityNot monotonic
2025-11-25T07:11:12.342785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.478
 
0.8%
4.058
 
0.8%
5.77
 
0.7%
4.957
 
0.7%
4.477
 
0.7%
4.757
 
0.7%
5.596
 
0.6%
5.16
 
0.6%
4.086
 
0.6%
4.076
 
0.6%
Other values (486)984
93.4%
ValueCountFrequency (%)
1.521
0.1%
1.711
0.1%
1.821
0.1%
1.851
0.1%
1.941
0.1%
2.141
0.1%
2.171
0.1%
2.191
0.1%
2.221
0.1%
2.231
0.1%
ValueCountFrequency (%)
24.681
0.1%
17.961
0.1%
15.781
0.1%
15.331
0.1%
14.81
0.1%
13.211
0.1%
13.031
0.1%
12.421
0.1%
12.071
0.1%
11.931
0.1%

Red blood cell count (×10⁶/µL)
Real number (ℝ)

High correlation 

Total RBC count

Distinct258
Distinct (%)24.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.6894106
Minimum2.86
Maximum7.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.390102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile3.74
Q14.32
median4.68
Q35.0325
95-th percentile5.6545
Maximum7.2
Range4.34
Interquartile range (IQR)0.7125

Descriptive statistics

Standard deviation0.57637507
Coefficient of variation (CV)0.12290992
Kurtosis0.40631493
Mean4.6894106
Median Absolute Deviation (MAD)0.36
Skewness0.073494999
Sum4933.26
Variance0.33220822
MonotonicityNot monotonic
2025-11-25T07:11:12.517155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9814
 
1.3%
4.5414
 
1.3%
4.4514
 
1.3%
4.5813
 
1.2%
4.8412
 
1.1%
4.4412
 
1.1%
4.8812
 
1.1%
4.4611
 
1.0%
4.7211
 
1.0%
4.611
 
1.0%
Other values (248)928
88.1%
ValueCountFrequency (%)
2.861
0.1%
2.941
0.1%
3.132
0.2%
3.181
0.1%
3.191
0.1%
3.21
0.1%
3.221
0.1%
3.231
0.1%
3.241
0.1%
3.291
0.1%
ValueCountFrequency (%)
7.21
0.1%
6.591
0.1%
6.451
0.1%
6.421
0.1%
6.361
0.1%
6.271
0.1%
6.231
0.1%
6.141
0.1%
6.11
0.1%
6.081
0.1%

Platelet count (×10³/µL)
Real number (ℝ)

Platelet count

Distinct306
Distinct (%)29.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean265.84791
Minimum7
Maximum884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.562283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile160.55
Q1214
median258
Q3303
95-th percentile409
Maximum884
Range877
Interquartile range (IQR)89

Descriptive statistics

Standard deviation82.110469
Coefficient of variation (CV)0.30886257
Kurtosis5.8713216
Mean265.84791
Median Absolute Deviation (MAD)44
Skewness1.3912982
Sum279672
Variance6742.1291
MonotonicityNot monotonic
2025-11-25T07:11:12.609216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26113
 
1.2%
21611
 
1.0%
24010
 
0.9%
28710
 
0.9%
2129
 
0.9%
2669
 
0.9%
2229
 
0.9%
2449
 
0.9%
2199
 
0.9%
2819
 
0.9%
Other values (296)954
90.6%
ValueCountFrequency (%)
71
0.1%
171
0.1%
421
0.1%
631
0.1%
801
0.1%
821
0.1%
871
0.1%
901
0.1%
961
0.1%
982
0.2%
ValueCountFrequency (%)
8841
0.1%
7511
0.1%
6761
0.1%
6521
0.1%
6191
0.1%
5751
0.1%
5521
0.1%
5491
0.1%
5441
0.1%
5421
0.1%

mch_pg
Real number (ℝ)

High correlation 

Mean corpuscular hemoglobin

Distinct138
Distinct (%)13.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean28.411597
Minimum16.7
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.656614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum16.7
5-th percentile23.1
Q127.1
median28.7
Q330.3
95-th percentile32.145
Maximum34
Range17.3
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation2.7248789
Coefficient of variation (CV)0.095907279
Kurtosis1.4802156
Mean28.411597
Median Absolute Deviation (MAD)1.6
Skewness-0.94437237
Sum29889
Variance7.4249653
MonotonicityNot monotonic
2025-11-25T07:11:12.703854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.625
 
2.4%
29.921
 
2.0%
28.921
 
2.0%
28.821
 
2.0%
27.920
 
1.9%
29.420
 
1.9%
27.519
 
1.8%
27.718
 
1.7%
27.218
 
1.7%
27.318
 
1.7%
Other values (128)851
80.8%
ValueCountFrequency (%)
16.71
0.1%
17.31
0.1%
17.61
0.1%
17.81
0.1%
17.91
0.1%
18.21
0.1%
18.41
0.1%
18.51
0.1%
18.81
0.1%
19.71
0.1%
ValueCountFrequency (%)
341
 
0.1%
33.91
 
0.1%
33.82
0.2%
33.71
 
0.1%
33.53
0.3%
33.42
0.2%
33.34
0.4%
33.21
 
0.1%
33.12
0.2%
332
0.2%

mchc_g_dL
Real number (ℝ)

High correlation 

Mean corpuscular hemoglobin concentration

Distinct59
Distinct (%)5.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean32.653707
Minimum28.3
Maximum35.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.751877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum28.3
5-th percentile31.2
Q132.2
median32.7
Q333.2
95-th percentile33.9
Maximum35.9
Range7.6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89265719
Coefficient of variation (CV)0.027337086
Kurtosis2.0274307
Mean32.653707
Median Absolute Deviation (MAD)0.5
Skewness-0.58209633
Sum34351.7
Variance0.79683686
MonotonicityNot monotonic
2025-11-25T07:11:12.802283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.660
 
5.7%
32.757
 
5.4%
32.454
 
5.1%
33.154
 
5.1%
3353
 
5.0%
33.252
 
4.9%
32.551
 
4.8%
32.850
 
4.7%
32.946
 
4.4%
32.344
 
4.2%
Other values (49)531
50.4%
ValueCountFrequency (%)
28.31
0.1%
28.61
0.1%
28.71
0.1%
28.81
0.1%
29.32
0.2%
29.41
0.1%
29.81
0.1%
302
0.2%
30.21
0.1%
30.32
0.2%
ValueCountFrequency (%)
35.91
 
0.1%
35.61
 
0.1%
35.31
 
0.1%
352
 
0.2%
34.94
 
0.4%
34.72
 
0.2%
34.61
 
0.1%
34.53
 
0.3%
34.410
0.9%
34.33
 
0.3%

Lymphocyte count (×10⁹/L)
Real number (ℝ)

High correlation 

Lymphocyte absolute count

Distinct295
Distinct (%)28.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.8269962
Minimum0.21
Maximum9.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.852324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.81
Q11.3
median1.73
Q32.24
95-th percentile3.1645
Maximum9.94
Range9.73
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.77925427
Coefficient of variation (CV)0.42652211
Kurtosis12.036719
Mean1.8269962
Median Absolute Deviation (MAD)0.47
Skewness1.8291566
Sum1922
Variance0.60723721
MonotonicityNot monotonic
2025-11-25T07:11:12.899536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8211
 
1.0%
1.5911
 
1.0%
1.5811
 
1.0%
1.811
 
1.0%
1.5311
 
1.0%
1.3311
 
1.0%
1.9311
 
1.0%
1.6610
 
0.9%
1.6110
 
0.9%
1.4410
 
0.9%
Other values (285)945
89.7%
ValueCountFrequency (%)
0.211
 
0.1%
0.221
 
0.1%
0.251
 
0.1%
0.271
 
0.1%
0.331
 
0.1%
0.372
0.2%
0.451
 
0.1%
0.463
0.3%
0.471
 
0.1%
0.491
 
0.1%
ValueCountFrequency (%)
9.941
0.1%
5.921
0.1%
5.321
0.1%
4.731
0.1%
4.681
0.1%
4.211
0.1%
4.21
0.1%
4.161
0.1%
4.141
0.1%
4.111
0.1%

Neutrophil count (×10⁹/L)
Real number (ℝ)

High correlation 

Neutrophil absolute count

Distinct393
Distinct (%)37.5%
Missing6
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.7616619
Minimum0.19
Maximum9.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:12.943334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile1.25
Q11.88
median2.52
Q33.35
95-th percentile5.311
Maximum9.31
Range9.12
Interquartile range (IQR)1.47

Descriptive statistics

Standard deviation1.2519029
Coefficient of variation (CV)0.45331506
Kurtosis2.9788724
Mean2.7616619
Median Absolute Deviation (MAD)0.68
Skewness1.4142769
Sum2891.46
Variance1.5672609
MonotonicityNot monotonic
2025-11-25T07:11:12.987688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.668
 
0.8%
1.858
 
0.8%
3.728
 
0.8%
2.648
 
0.8%
2.368
 
0.8%
2.717
 
0.7%
2.497
 
0.7%
2.27
 
0.7%
2.157
 
0.7%
1.937
 
0.7%
Other values (383)972
92.3%
ValueCountFrequency (%)
0.191
0.1%
0.652
0.2%
0.721
0.1%
0.811
0.1%
0.862
0.2%
0.91
0.1%
0.922
0.2%
0.962
0.2%
0.992
0.2%
11
0.1%
ValueCountFrequency (%)
9.311
0.1%
9.171
0.1%
8.521
0.1%
8.071
0.1%
7.961
0.1%
7.661
0.1%
7.431
0.1%
7.41
0.1%
71
0.1%
6.991
0.1%

Monocyte count (×10⁹/L)
Real number (ℝ)

High correlation 

Monocyte absolute count

Distinct96
Distinct (%)9.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.4538308
Minimum0.11
Maximum2.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:13.030654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile0.22
Q10.33
median0.42
Q30.54
95-th percentile0.79
Maximum2.12
Range2.01
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.19087226
Coefficient of variation (CV)0.42058022
Kurtosis10.270507
Mean0.4538308
Median Absolute Deviation (MAD)0.1
Skewness2.0631299
Sum477.43
Variance0.036432219
MonotonicityNot monotonic
2025-11-25T07:11:13.079114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4333
 
3.1%
0.3433
 
3.1%
0.4132
 
3.0%
0.431
 
2.9%
0.3830
 
2.8%
0.3630
 
2.8%
0.3529
 
2.8%
0.4829
 
2.8%
0.2928
 
2.7%
0.4628
 
2.7%
Other values (86)749
71.1%
ValueCountFrequency (%)
0.114
 
0.4%
0.122
 
0.2%
0.143
 
0.3%
0.154
 
0.4%
0.163
 
0.3%
0.171
 
0.1%
0.186
0.6%
0.194
 
0.4%
0.26
0.6%
0.2112
1.1%
ValueCountFrequency (%)
2.121
0.1%
1.841
0.1%
1.541
0.1%
1.471
0.1%
1.421
0.1%
1.181
0.1%
1.171
0.1%
1.081
0.1%
1.072
0.2%
1.041
0.1%

Eosinophil count (×10⁹/L)
Real number (ℝ)

Zeros 

Eosinophil absolute count

Distinct88
Distinct (%)8.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.14214829
Minimum0
Maximum3.16
Zeros47
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:13.128963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.03
median0.075
Q30.16
95-th percentile0.51
Maximum3.16
Range3.16
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.22564749
Coefficient of variation (CV)1.5874091
Kurtosis44.797394
Mean0.14214829
Median Absolute Deviation (MAD)0.055
Skewness5.3004426
Sum149.54
Variance0.050916789
MonotonicityNot monotonic
2025-11-25T07:11:13.179036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0187
 
8.3%
0.0285
 
8.1%
0.0370
 
6.6%
0.0762
 
5.9%
0.0459
 
5.6%
0.0659
 
5.6%
0.0557
 
5.4%
047
 
4.5%
0.0844
 
4.2%
0.1240
 
3.8%
Other values (78)442
42.0%
ValueCountFrequency (%)
047
4.5%
0.0187
8.3%
0.0285
8.1%
0.0370
6.6%
0.0459
5.6%
0.0557
5.4%
0.0659
5.6%
0.0762
5.9%
0.0844
4.2%
0.0937
3.5%
ValueCountFrequency (%)
3.161
0.1%
1.891
0.1%
1.752
0.2%
1.571
0.1%
1.511
0.1%
1.481
0.1%
1.351
0.1%
1.251
0.1%
1.232
0.2%
1.151
0.1%

Basophil count (×10⁹/L)
Real number (ℝ)

Zeros 

Basophil absolute count

Distinct15
Distinct (%)1.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.024030418
Minimum0
Maximum0.21
Zeros122
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:13.221401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.03
95-th percentile0.05
Maximum0.21
Range0.21
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.019359659
Coefficient of variation (CV)0.80563139
Kurtosis13.357364
Mean0.024030418
Median Absolute Deviation (MAD)0.01
Skewness2.4211104
Sum25.28
Variance0.00037479641
MonotonicityNot monotonic
2025-11-25T07:11:13.274520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.02293
27.8%
0.03217
20.6%
0.01217
20.6%
0122
11.6%
0.04100
 
9.5%
0.0551
 
4.8%
0.0619
 
1.8%
0.0712
 
1.1%
0.096
 
0.6%
0.085
 
0.5%
Other values (5)10
 
0.9%
ValueCountFrequency (%)
0122
11.6%
0.01217
20.6%
0.02293
27.8%
0.03217
20.6%
0.04100
 
9.5%
0.0551
 
4.8%
0.0619
 
1.8%
0.0712
 
1.1%
0.085
 
0.5%
0.096
 
0.6%
ValueCountFrequency (%)
0.211
 
0.1%
0.161
 
0.1%
0.132
 
0.2%
0.114
 
0.4%
0.12
 
0.2%
0.096
 
0.6%
0.085
 
0.5%
0.0712
 
1.1%
0.0619
 
1.8%
0.0551
4.8%

cd4_correction_applied
Categorical

Constant 

Quality flag: CD4 corrections applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
0.0
1053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3159
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01053
100.0%

Length

2025-11-25T07:11:13.318801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:13.350549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01053
100.0%

Most occurring characters

ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2106
66.7%
Other Punctuation1053
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02106
100.0%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

final_comprehensive_fix_applied
Categorical

Constant 

Quality flag: Comprehensive corrections applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
1.0
1053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3159
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01053
100.0%

Length

2025-11-25T07:11:13.385364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:13.417725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01053
100.0%

Most occurring characters

ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2106
66.7%
Other Punctuation1053
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11053
50.0%
01053
50.0%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

waist_circ_unit_correction_applied
Boolean

Constant 

Quality flag: Waist circumference unit corrected

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
False
1053 
ValueCountFrequency (%)
False1053
100.0%
2025-11-25T07:11:13.443509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

sa_biomarker_standards
Categorical

Constant 

South African biomarker reference standards applied

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
1.0
1053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3159
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01053
100.0%

Length

2025-11-25T07:11:13.476811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:13.510290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01053
100.0%

Most occurring characters

ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2106
66.7%
Other Punctuation1053
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11053
50.0%
01053
50.0%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11053
33.3%
.1053
33.3%
01053
33.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Daily mean temperature

Distinct17
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.561754
Minimum11.215
Maximum20.448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:13.540238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.215
5-th percentile11.215
Q113.247
median15.298
Q318.567
95-th percentile20.448
Maximum20.448
Range9.233
Interquartile range (IQR)5.32

Descriptive statistics

Standard deviation3.1473627
Coefficient of variation (CV)0.20224987
Kurtosis-1.3532273
Mean15.561754
Median Absolute Deviation (MAD)2.491
Skewness0.21908974
Sum16386.527
Variance9.905892
MonotonicityNot monotonic
2025-11-25T07:11:13.579451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13.439126
12.0%
20.056110
10.4%
18.56793
8.8%
15.29891
8.6%
11.28588
8.4%
13.16786
8.2%
11.21581
7.7%
16.50472
 
6.8%
20.44861
 
5.8%
13.24759
 
5.6%
Other values (7)186
17.7%
ValueCountFrequency (%)
11.21581
7.7%
11.28588
8.4%
13.16786
8.2%
13.24759
5.6%
13.439126
12.0%
13.7346
 
0.6%
14.05627
 
2.6%
14.30138
 
3.6%
14.8313
 
1.2%
15.29891
8.6%
ValueCountFrequency (%)
20.44861
5.8%
20.056110
10.4%
19.73912
 
1.1%
19.63344
 
4.2%
18.56793
8.8%
17.78946
4.4%
16.50472
6.8%
15.29891
8.6%
14.8313
 
1.2%
14.30138
 
3.6%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Daily maximum temperature

Distinct17
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.97633
Minimum15.722
Maximum26.904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:13.615671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.722
5-th percentile17.385
Q119.286
median22.913
Q325.104
95-th percentile25.493
Maximum26.904
Range11.182
Interquartile range (IQR)5.818

Descriptive statistics

Standard deviation2.9325139
Coefficient of variation (CV)0.13343966
Kurtosis-1.1593326
Mean21.97633
Median Absolute Deviation (MAD)2.438
Skewness-0.22422004
Sum23141.075
Variance8.599638
MonotonicityNot monotonic
2025-11-25T07:11:13.651865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
19.286126
12.0%
25.493110
10.4%
23.03293
8.8%
22.91391
8.6%
18.81288
8.4%
20.81986
8.2%
17.38581
7.7%
25.35172
 
6.8%
25.31261
 
5.8%
20.8159
 
5.6%
Other values (7)186
17.7%
ValueCountFrequency (%)
15.72227
 
2.6%
17.38581
7.7%
18.81288
8.4%
19.286126
12.0%
19.47713
 
1.2%
20.8159
5.6%
20.81986
8.2%
21.6646
 
0.6%
22.07938
 
3.6%
22.91391
8.6%
ValueCountFrequency (%)
26.90412
 
1.1%
25.493110
10.4%
25.35172
6.8%
25.31261
5.8%
25.10446
4.4%
25.09744
 
4.2%
23.03293
8.8%
22.91391
8.6%
22.07938
 
3.6%
21.6646
 
0.6%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Daily minimum temperature

Distinct17
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7010076
Minimum4.536
Maximum16.914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:13.688398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.536
5-th percentile4.536
Q16.622
median8.121
Q314.389
95-th percentile16.914
Maximum16.914
Range12.378
Interquartile range (IQR)7.767

Descriptive statistics

Standard deviation4.2586607
Coefficient of variation (CV)0.43899158
Kurtosis-1.2921288
Mean9.7010076
Median Absolute Deviation (MAD)3.4
Skewness0.50830913
Sum10215.161
Variance18.136191
MonotonicityNot monotonic
2025-11-25T07:11:13.727205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6.622126
12.0%
16.914110
10.4%
15.34993
8.8%
8.35191
8.6%
4.53688
8.4%
8.12186
8.2%
6.62681
7.7%
6.12172
 
6.8%
14.38961
 
5.8%
4.72159
 
5.6%
Other values (7)186
17.7%
ValueCountFrequency (%)
4.53688
8.4%
4.72159
5.6%
5.536
 
0.6%
6.12172
6.8%
6.622126
12.0%
6.62681
7.7%
7.1638
 
3.6%
8.12186
8.2%
8.35191
8.6%
10.18712
 
1.1%
ValueCountFrequency (%)
16.914110
10.4%
15.34993
8.8%
14.92244
 
4.2%
14.38961
5.8%
12.39127
 
2.6%
11.88346
4.4%
10.48813
 
1.2%
10.18712
 
1.1%
8.35191
8.6%
8.12186
8.2%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Temperature anomaly from baseline

Distinct17
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6150627
Minimum-3.11
Maximum9.491
Zeros0
Zeros (%)0.0%
Negative27
Negative (%)2.6%
Memory size16.5 KiB
2025-11-25T07:11:13.764374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.11
5-th percentile1.389
Q14.429
median5.917
Q37.251
95-th percentile7.846
Maximum9.491
Range12.601
Interquartile range (IQR)2.822

Descriptive statistics

Standard deviation2.2579816
Coefficient of variation (CV)0.40212936
Kurtosis3.8005294
Mean5.6150627
Median Absolute Deviation (MAD)1.334
Skewness-1.7706503
Sum5912.661
Variance5.0984809
MonotonicityNot monotonic
2025-11-25T07:11:13.805320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.093126
12.0%
4.429110
10.4%
3.0893
8.8%
5.89991
8.6%
7.25188
8.4%
7.41386
8.2%
5.91781
7.7%
7.84672
 
6.8%
5.37761
 
5.8%
1.38959
 
5.6%
Other values (7)186
17.7%
ValueCountFrequency (%)
-3.1127
 
2.6%
1.38959
5.6%
3.0893
8.8%
4.00513
 
1.2%
4.429110
10.4%
5.37761
5.8%
5.89991
8.6%
5.91781
7.7%
6.54438
 
3.6%
6.77544
 
4.2%
ValueCountFrequency (%)
9.4916
 
0.6%
7.84672
6.8%
7.712
 
1.1%
7.41386
8.2%
7.25188
8.4%
7.093126
12.0%
6.83846
 
4.4%
6.77544
 
4.2%
6.54438
 
3.6%
5.91781
7.7%

climate_heat_day_p90
Categorical

Constant 

Heat day indicator (>90th percentile)

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
0.0
1053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3159
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01053
100.0%

Length

2025-11-25T07:11:13.852872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:13.886766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01053
100.0%

Most occurring characters

ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2106
66.7%
Other Punctuation1053
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02106
100.0%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

climate_heat_day_p95
Categorical

Constant 

Heat day indicator (>95th percentile)

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
0.0
1053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3159
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.01053
100.0%

Length

2025-11-25T07:11:13.921424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:13.952605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01053
100.0%

Most occurring characters

ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2106
66.7%
Other Punctuation1053
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02106
100.0%
Other Punctuation
ValueCountFrequency (%)
.1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02106
66.7%
.1053
33.3%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Heat stress index

Distinct17
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.935821
Minimum10.742
Maximum23.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2025-11-25T07:11:13.982619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.742
5-th percentile13.476
Q113.864
median16.598
Q319.468
95-th percentile22.279
Maximum23.6
Range12.858
Interquartile range (IQR)5.604

Descriptive statistics

Standard deviation2.9671094
Coefficient of variation (CV)0.17519726
Kurtosis-0.89614382
Mean16.935821
Median Absolute Deviation (MAD)2.816
Skewness0.18692715
Sum17833.419
Variance8.8037382
MonotonicityNot monotonic
2025-11-25T07:11:14.021347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
15.87126
12.0%
19.907110
10.4%
19.46893
8.8%
13.78291
8.6%
13.86488
8.4%
15.40886
8.2%
13.47681
7.7%
19.35472
 
6.8%
22.27961
 
5.8%
16.67659
 
5.6%
Other values (7)186
17.7%
ValueCountFrequency (%)
10.74227
 
2.6%
13.47681
7.7%
13.4913
 
1.2%
13.78291
8.6%
13.86488
8.4%
15.40886
8.2%
15.87126
12.0%
16.59846
 
4.4%
16.67659
5.6%
17.47138
 
3.6%
ValueCountFrequency (%)
23.612
 
1.1%
22.27961
5.8%
21.0344
 
4.2%
19.907110
10.4%
19.46893
8.8%
19.35472
6.8%
17.4916
 
0.6%
17.47138
 
3.6%
16.67659
5.6%
16.59846
4.4%

climate_season
Categorical

High correlation 

Season

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.8 KiB
Summer
303 
Winter
295 
Autumn
233 
Spring
222 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6318
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowSummer
3rd rowSummer
4th rowSummer
5th rowSummer

Common Values

ValueCountFrequency (%)
Summer303
28.8%
Winter295
28.0%
Autumn233
22.1%
Spring222
21.1%

Length

2025-11-25T07:11:14.063593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:14.100396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
summer303
28.8%
winter295
28.0%
autumn233
22.1%
spring222
21.1%

Most occurring characters

ValueCountFrequency (%)
m839
13.3%
r820
13.0%
u769
12.2%
n750
11.9%
e598
9.5%
t528
8.4%
S525
8.3%
i517
8.2%
W295
 
4.7%
A233
 
3.7%
Other values (2)444
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5265
83.3%
Uppercase Letter1053
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m839
15.9%
r820
15.6%
u769
14.6%
n750
14.2%
e598
11.4%
t528
10.0%
i517
9.8%
p222
 
4.2%
g222
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
S525
49.9%
W295
28.0%
A233
22.1%

Most occurring scripts

ValueCountFrequency (%)
Latin6318
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m839
13.3%
r820
13.0%
u769
12.2%
n750
11.9%
e598
9.5%
t528
8.4%
S525
8.3%
i517
8.2%
W295
 
4.7%
A233
 
3.7%
Other values (2)444
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m839
13.3%
r820
13.0%
u769
12.2%
n750
11.9%
e598
9.5%
t528
8.4%
S525
8.3%
i517
8.2%
W295
 
4.7%
A233
 
3.7%
Other values (2)444
7.0%

Interactions

2025-11-25T07:11:09.565066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:45.143611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:46.186015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:47.117328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:47.972263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:48.959480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:49.834015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:50.794249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:51.682114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:52.704537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:53.685004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:54.576358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:55.586687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:56.491564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:57.396538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:58.378512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:59.262512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:00.239793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:01.158991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:02.044603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:03.001689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:03.928894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:04.939150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:05.826033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:06.698612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:07.670621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:08.579297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:09.595961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:45.185362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:46.216820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:47.147055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:48.004337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:48.991279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:49.862959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:50.827455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:51.716414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:52.735539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:53.716136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:54.609823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:55.619467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:56.523092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:57.430320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:58.410692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:59.294280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:00.271897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:01.189885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:02.075583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:03.034545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:03.960926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:04.969538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:05.856306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:06.728268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:07.701607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:08.610701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:09.632561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:45.226302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:46.251150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:47.178946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:48.038187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:49.025762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:49.895658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:50.864175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:51.756403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:52.770284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:53.751159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:54.646671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:55.654920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:56.556580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:57.464505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:58.445713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:59.330027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:00.306586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:01.225608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:02.108967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:03.070199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:03.996420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:05.004589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:05.890987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:06.763299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:07.737151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:08.645851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:09.664745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:45.255643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:46.281842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:47.209306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:48.070056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T07:10:52.630338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:53.618787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:54.509706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:55.516005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:56.425465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:57.325997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:58.309438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:59.195830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:00.173378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:01.087948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:01.978712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:02.936217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:03.858270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:04.869875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:05.760511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:06.630527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:07.603131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:08.510649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:09.497087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:10.435431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:46.153071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:47.082226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:47.941211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:48.924998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:49.799787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:50.753234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:51.648361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:52.666797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:53.651553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:54.541807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:55.552365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:56.459451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:57.360069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:58.342466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:59.229008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:00.205584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:01.123098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:02.012000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:02.969408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:03.893816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:04.903545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:05.792740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:06.664666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:07.636608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:08.544058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:09.530613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T07:11:14.229097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)Basophil count (×10⁹/L)CD4 cell count (cells/µL)Eosinophil count (×10⁹/L)HIV viral load (copies/mL)Hematocrit (%)Lymphocyte count (×10⁹/L)Monocyte count (×10⁹/L)Neutrophil count (×10⁹/L)Platelet count (×10³/µL)Red blood cell count (×10⁶/µL)SexWhite blood cell count (×10³/µL)body_temperature_celsiusclimate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_stress_indexclimate_seasonclimate_temp_anomalydiastolic_bp_mmHgheart_rate_bpmheight_mhemoglobin_g_dLmch_pgmchc_g_dLsystolic_bp_mmHgweight_kg
Age (at enrolment)1.0000.1630.009-0.1610.0040.054-0.080-0.111-0.035-0.106-0.055-0.1140.142-0.1310.0130.0440.0510.0880.0400.020-0.0240.3140.0340.103-0.0720.0510.0200.2770.231
BMI (kg/m²)0.1631.0000.0120.0870.004-0.141-0.1210.1260.0630.0550.095-0.0980.3630.0880.0450.0400.0560.0750.0780.023-0.0450.2000.120-0.360-0.111-0.011-0.0000.1320.800
Basophil count (×10⁹/L)0.0090.0121.0000.1180.174-0.1440.0750.3900.2360.1830.1510.0560.0000.3490.002-0.069-0.098-0.073-0.0940.1010.120-0.005-0.030-0.0150.0660.021-0.0270.0100.003
CD4 cell count (cells/µL)-0.1610.0870.1181.000-0.019-0.5070.1740.182-0.0540.1640.0650.1230.0720.179-0.1000.0150.0250.0150.0220.0000.009-0.005-0.206-0.0810.1700.1050.0340.0060.051
Eosinophil count (×10⁹/L)0.0040.0040.174-0.0191.0000.0550.0530.1250.079-0.0350.0020.0160.0370.115-0.0910.0360.0090.0100.0240.0000.026-0.043-0.0980.0510.0520.0540.009-0.0120.032
HIV viral load (copies/mL)0.054-0.141-0.144-0.5070.0551.000-0.093-0.2460.072-0.077-0.061-0.0850.052-0.1390.081-0.065-0.077-0.047-0.0770.000-0.014-0.0280.1920.061-0.090-0.0030.032-0.079-0.114
Hematocrit (%)-0.080-0.1210.0750.1740.053-0.0931.0000.1070.0280.050-0.2210.8140.5680.088-0.073-0.054-0.045-0.027-0.0620.040-0.0710.107-0.3060.3460.9790.3330.2300.1710.087
Lymphocyte count (×10⁹/L)-0.1110.1260.3900.1820.125-0.2460.1071.0000.3570.1900.1380.0700.0670.624-0.026-0.031-0.037-0.006-0.0400.0300.0260.027-0.057-0.0690.1020.069-0.0050.0150.097
Monocyte count (×10⁹/L)-0.0350.0630.236-0.0540.0790.0720.0280.3571.0000.4390.1520.0180.0000.6030.158-0.012-0.037-0.024-0.0400.0000.0360.0330.118-0.0120.0250.005-0.0260.0620.061
Neutrophil count (×10⁹/L)-0.1060.0550.1830.164-0.035-0.0770.0500.1900.4391.0000.2550.0510.0000.8370.104-0.041-0.047-0.015-0.0540.0220.021-0.0340.121-0.0670.051-0.015-0.0290.0110.016
Platelet count (×10³/µL)-0.0550.0950.1510.0650.002-0.061-0.2210.1380.1520.2551.000-0.1660.1740.2580.0250.0050.0170.0080.0230.0510.038-0.0860.161-0.162-0.227-0.138-0.137-0.089-0.010
Red blood cell count (×10⁶/µL)-0.114-0.0980.0560.1230.016-0.0850.8140.0700.0180.051-0.1661.0000.4710.073-0.073-0.053-0.054-0.041-0.0640.046-0.0090.097-0.2400.3030.756-0.189-0.0310.1680.082
Sex0.1420.3630.0000.0720.0370.0520.5680.0670.0000.0000.1740.4711.0000.0170.0000.0000.0360.0000.0540.0500.0260.0360.3010.5860.5670.1770.1730.1970.159
White blood cell count (×10³/µL)-0.1310.0880.3490.1790.115-0.1390.0880.6240.6030.8370.2580.0730.0171.0000.092-0.047-0.057-0.018-0.0640.0000.034-0.0090.075-0.0660.0850.019-0.0250.0290.055
body_temperature_celsius0.0130.0450.002-0.100-0.0910.081-0.073-0.0260.1580.1040.025-0.0730.0000.0921.0000.1350.1480.1260.0850.096-0.154-0.0100.246-0.058-0.078-0.029-0.021-0.0180.023
climate_daily_max_temp0.0440.040-0.0690.0150.036-0.065-0.054-0.031-0.012-0.0410.005-0.0530.000-0.0470.1351.0000.8870.6180.8050.686-0.123-0.0150.012-0.095-0.062-0.028-0.091-0.036-0.007
climate_daily_mean_temp0.0510.056-0.0980.0250.009-0.077-0.045-0.037-0.037-0.0470.017-0.0540.036-0.0570.1480.8871.0000.7650.8180.740-0.380-0.0150.019-0.141-0.056-0.014-0.119-0.038-0.017
climate_daily_min_temp0.0880.075-0.0730.0150.010-0.047-0.027-0.006-0.024-0.0150.008-0.0410.000-0.0180.1260.6180.7651.0000.4950.810-0.5550.016-0.019-0.162-0.045-0.013-0.137-0.018-0.016
climate_heat_stress_index0.0400.078-0.0940.0220.024-0.077-0.062-0.040-0.040-0.0540.023-0.0640.054-0.0640.0850.8050.8180.4951.0000.660-0.191-0.0160.009-0.150-0.073-0.032-0.126-0.015-0.006
climate_season0.0200.0230.1010.0000.0000.0000.0400.0300.0000.0220.0510.0460.0500.0000.0960.6860.7400.8100.6601.0000.6280.0620.0780.1330.0000.0990.1770.0280.045
climate_temp_anomaly-0.024-0.0450.1200.0090.026-0.014-0.0710.0260.0360.0210.038-0.0090.0260.034-0.154-0.123-0.380-0.555-0.1910.6281.000-0.0040.0120.122-0.068-0.092-0.0060.0180.017
diastolic_bp_mmHg0.3140.200-0.005-0.005-0.043-0.0280.1070.0270.033-0.034-0.0860.0970.036-0.009-0.010-0.015-0.0150.016-0.0160.062-0.0041.0000.0170.1260.1140.0450.0640.7120.275
heart_rate_bpm0.0340.120-0.030-0.206-0.0980.192-0.306-0.0570.1180.1210.161-0.2400.3010.0750.2460.0120.019-0.0190.0090.0780.0120.0171.000-0.161-0.302-0.142-0.075-0.1040.029
height_m0.103-0.360-0.015-0.0810.0510.0610.346-0.069-0.012-0.067-0.1620.3030.586-0.066-0.058-0.095-0.141-0.162-0.1500.1330.1220.126-0.1611.0000.3410.0870.0830.2110.228
hemoglobin_g_dL-0.072-0.1110.0660.1700.052-0.0900.9790.1020.0250.051-0.2270.7560.5670.085-0.078-0.062-0.056-0.045-0.0730.000-0.0680.114-0.3020.3411.0000.4360.3920.1770.096
mch_pg0.051-0.0110.0210.1050.054-0.0030.3330.0690.005-0.015-0.138-0.1890.1770.019-0.029-0.028-0.014-0.013-0.0320.099-0.0920.045-0.1420.0870.4361.0000.6790.0400.047
mchc_g_dL0.020-0.000-0.0270.0340.0090.0320.230-0.005-0.026-0.029-0.137-0.0310.173-0.025-0.021-0.091-0.119-0.137-0.1260.177-0.0060.064-0.0750.0830.3920.6791.0000.0650.049
systolic_bp_mmHg0.2770.1320.0100.006-0.012-0.0790.1710.0150.0620.011-0.0890.1680.1970.029-0.018-0.036-0.038-0.018-0.0150.0280.0180.712-0.1040.2110.1770.0400.0651.0000.261
weight_kg0.2310.8000.0030.0510.032-0.1140.0870.0970.0610.016-0.0100.0820.1590.0550.023-0.007-0.017-0.016-0.0060.0450.0170.2750.0290.2280.0960.0470.0490.2611.000

Missing values

2025-11-25T07:11:10.501863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T07:11:10.664862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T07:11:10.777843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceAge (at enrolment)Sexprimary_dateBMI (kg/m²)weight_kgheight_msystolic_bp_mmHgdiastolic_bp_mmHgheart_rate_bpmbody_temperature_celsiusCD4 cell count (cells/µL)HIV viral load (copies/mL)Hematocrit (%)hemoglobin_g_dLWhite blood cell count (×10³/µL)Red blood cell count (×10⁶/µL)Platelet count (×10³/µL)mch_pgmchc_g_dLLymphocyte count (×10⁹/L)Neutrophil count (×10⁹/L)Monocyte count (×10⁹/L)Eosinophil count (×10⁹/L)Basophil count (×10⁹/L)cd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
2162JHB_Ezin_00230.0Male2017-01-1723.91623966.71.67133.082.061.036.735.06641.046.014.85.895.62294.026.331.91.833.600.370.060.020.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
2163JHB_Ezin_00234.0Male2017-01-2327.64530877.11.67130.084.060.036.128.023851.043.014.32.904.84261.029.532.91.261.360.240.020.020.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
2164JHB_Ezin_00244.0Male2017-01-1923.83624880.71.84131.083.068.036.125.758961.050.016.64.505.46216.030.433.02.071.790.450.100.090.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
2165JHB_Ezin_00225.0Female2017-01-1945.289256123.31.65119.067.074.036.425.85903.038.012.15.704.56281.026.531.42.332.820.440.060.040.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
2166JHB_Ezin_00220.0Female2017-01-2428.66401874.31.61114.071.082.036.112.4815081.037.012.05.554.10188.029.432.61.971.770.421.350.030.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
2167JHB_Ezin_00233.0Female2017-01-3030.85937579.01.60138.084.081.036.232.59680.040.013.55.674.45239.030.433.61.663.320.560.110.030.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
2168JHB_Ezin_00223.0Female2017-02-0120.87005352.11.5896.067.086.037.041.4512806.042.014.03.904.93238.028.433.01.192.460.200.040.000.01.0False1.020.44825.31214.3895.3770.00.022.279Summer
2169JHB_Ezin_00230.0Female2017-01-2423.55555653.01.50103.058.081.036.88.81179182.031.010.12.193.75266.027.032.80.711.120.270.070.030.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
2170JHB_Ezin_00223.0Female2017-01-2431.95312581.81.60106.078.063.036.125.4010611.033.010.73.014.37260.024.532.21.091.520.320.060.020.01.0False1.019.73926.90410.1877.7000.00.023.600Summer
2171JHB_Ezin_00220.0Female2017-02-0118.85038750.71.64108.061.081.036.117.89443901.037.011.63.624.35154.026.731.62.680.650.290.000.000.01.0False1.020.44825.31214.3895.3770.00.022.279Summer
study_sourceAge (at enrolment)Sexprimary_dateBMI (kg/m²)weight_kgheight_msystolic_bp_mmHgdiastolic_bp_mmHgheart_rate_bpmbody_temperature_celsiusCD4 cell count (cells/µL)HIV viral load (copies/mL)Hematocrit (%)hemoglobin_g_dLWhite blood cell count (×10³/µL)Red blood cell count (×10⁶/µL)Platelet count (×10³/µL)mch_pgmchc_g_dLLymphocyte count (×10⁹/L)Neutrophil count (×10⁹/L)Monocyte count (×10⁹/L)Eosinophil count (×10⁹/L)Basophil count (×10⁹/L)cd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
3205JHB_Ezin_00218.0Female2017-05-1731.09261274.71.55102.071.077.036.718.192461.041.013.94.954.55249.030.533.42.621.930.320.030.050.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
3206JHB_Ezin_00218.0Male2017-03-2015.51514345.91.7299.064.072.036.32.6777566.041.012.93.045.39278.023.931.51.111.510.200.210.010.01.0False1.017.78925.10411.8836.8380.00.016.598Autumn
3207JHB_Ezin_00215.0Male2017-08-0417.99802843.81.56112.072.076.036.12.86361889.038.012.66.294.71216.026.833.12.742.910.480.110.050.01.0False1.013.43919.2866.6227.0930.00.015.870Winter
3208JHB_Ezin_00218.0Male2017-09-1320.40816357.61.68125.085.078.036.527.5251443.049.016.44.425.80312.028.333.41.262.610.520.020.020.01.0False1.016.50425.3516.1217.8460.00.019.354Spring
3209JHB_Ezin_00218.0Female2017-09-2029.65862578.81.63112.082.081.036.06.908022.039.013.05.814.88384.026.633.12.782.230.380.390.030.01.0False1.016.50425.3516.1217.8460.00.019.354Spring
3210JHB_Ezin_00218.0Male2017-10-0220.45196364.81.78119.076.090.036.714.611295.045.014.78.315.17193.028.532.92.395.240.630.020.030.01.0False1.015.29822.9138.3515.8990.00.013.782Spring
3211JHB_Ezin_00217.0Male2017-11-2218.80047258.91.77135.083.079.036.318.437856.045.015.34.475.21261.029.433.71.362.620.370.100.020.01.0False1.013.24720.8104.7211.3890.00.016.676Spring
3212JHB_Ezin_00218.0Female2017-11-1621.69314160.51.67114.068.058.036.434.9126688.041.013.33.794.34272.030.732.82.101.420.150.100.020.01.0False1.013.24720.8104.7211.3890.00.016.676Spring
3213JHB_Ezin_00215.0Female2018-02-1422.24723051.41.5297.062.076.036.60.63170902.037.012.23.384.21231.028.932.60.222.360.310.490.000.01.0False1.018.56723.03215.3493.0800.00.019.468Summer
3214JHB_Ezin_00218.0Female2018-05-0424.72898464.11.61113.082.075.036.423.6861862.037.011.74.844.26344.027.431.52.162.190.410.070.020.01.0False1.013.73421.6645.5309.4910.00.017.491Autumn